Infrared (IR) imaging devices, such as IR cameras, can be used to find gas in various applications. For example, the IR camera manufacturer FLIR has a cooled gas camera that is used for finding many different gases.
Detecting and visualizing gas using IR techniques can be difficult since IR imaging devices typically can detect and represent 65,000 thermal levels in a radiometric IR image but only have 255 colors to represent this data on the display. First, the gas detection in current prior art tends to be resource consuming with regard to processing power due to complicated operations required to detect gas present in an IR image. Secondly, the visualization of gas in an IR image also requires some kind of translation between the high resolution radiometric IR image and a displayed IR image. It is possible to work with level and span to visualize some smaller portion of these 65,000 levels onto the 255 color scale, this is however quite time consuming and it can be hard to adjust to the current gas present in an imaged scene. Furthermore, if there are large differences in temperature between objects in the imaged scene, pixels having relatively small temperature differences will be visualized as having the same color, or very similar colors. Thereby, the color difference between gas and surrounding pixels in the image may be non-existent or very small, meaning that it is not possible, or very hard, to discern the visualized gas in the image with the human eye.
Examples of related art are found in U.S. Pat. Nos. 5,656,813, 7,649,174, 5,656,813, and 7,649,174.
While the prior art is directed to gas detection, it is deficient because the conventional methods require too much computational power, do not provide accurate gas detection, and/or do not provide sufficient visualization of the detected gas.
Therefore, there is still a need for improvements in passive camera systems in order to increase the detection capability in terms of distinguishing between an infrared absorbing gas cloud and background elements as well as improved visualization in a computationally efficient matter.